A binary mixed integer coded genetic algorithm for multi-objective optimization of nuclear research reactor fuel reloading
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Do Quang Binh
, Ngo Quang Huy and Nguyen Hoang Hai
Abstract
This paper presents a new approach based on a binary mixed integer coded genetic algorithm in conjunction with the weighted sum method for multi-objective optimization of fuel loading patterns for nuclear research reactors. The proposed genetic algorithm works with two types of chromosomes: binary and integer chromosomes, and consists of two types of genetic operators: one working on binary chromosomes and the other working on integer chromosomes. The algorithm automatically searches for the most suitable weighting factors of the weighting function and the optimal fuel loading patterns in the search process. Illustrative calculations are implemented for a research reactor type TRIGA MARK II loaded with the Russian VVR-M2 fuels. Results show that the proposed genetic algorithm can successfully search for both the best weighting factors and a set of approximate optimal loading patterns that maximize the effective multiplication factor and minimize the power peaking factor while satisfying operational and safety constraints for the research reactor.
Kurzfassung
In der vorliegenden Arbeit wird ein neuer Ansatz vorgestellt, der auf einem binärkodierten gemischt-ganzzahligen genetischen Algorithmus basiert in Verbindung mit der gewichteten Summenmethode für die multiobjektive Optimierung des Beladungsschemas der Brennelemente eines Forschungsreaktors. Der vorgeschlagene genetische Algorithmus arbeitet mit zwei Arten genetischer Operatoren: einem auf der Grundlage binärer Chromosomen und einem anderen auf der Grundlage ganzzahliger Chromosomen. Der Algorithmus sucht automatisch nach den am besten geeigneten Wichtungsfunktionen und dem optimalen Beladungsschema für die Brennelemente. Illustrative Berechnungen werden durchgeführt für einen Forschungsreaktor vom Typ TRIGA MARK II beladen mit russischen VVR-M2 Brennelementen. Die Ergebnisse zeigen, dass der vorgeschlagene genetische Algorithmus erfolgreich die besten Wichtungsfaktoren und eine Reihe optimaler Beladungsschemata sucht, wodurch der effektive Multiplikationsfaktor maximiert und der Leistungsspitzenfaktor minimiert wird, unter Einhaltung betrieblicher Beschränkungen und Sicherheitsauflagen des Forschungsreaktors.
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Articles in the same Issue
- Contents/Inhalt
- Contents
- Summaries/Kurzfassungen
- Summaries
- Technical Contributions/Fachbeiträge
- Evaluated gas production cross-section data for natural titanium irradiated with protons at energies up to 3 GeV
- Investigation of (n,γ) reaction in hybrid reactor zones
- Burnup analysis of the VVER-1000 reactor using thorium-based fuel
- Equivalent thermal conductivity of the storage basket with spent nuclear fuel of VVER-1000 reactors
- A study on empirical systematic for the (d,n) reaction cross sections at 8.6 MeV
- Recursive solutions for multi-group neutron kinetics diffusion equations in homogeneous three-dimensional rectangular domains with time dependent perturbations
- Thermoluminescent characteristics of nano-structure hydroxyapatite:Dy
- Performance evaluation of anion exchange resins Purolite NRW-5050 and Duolite A-611 by application of radioisotopic techniques
- A binary mixed integer coded genetic algorithm for multi-objective optimization of nuclear research reactor fuel reloading
- Development of an educational nuclear research reactor simulator
- Technical Notes/Technische Mitteilungen
- Obtaining the neutronic and thermal hydraulic parameters of the VVER-1000 Bushehr nuclear reactor core by coupling nuclear codes